Consequently, recent papers have attempted to incorporate physical knowledge into these data-based approaches. Several works have directly included the governing equations of a dynamical system within the loss function of neural networks. This led for instance to the so-called Physics-Informed Neural Networks (PINN) [1]}, used for a wide range of physics-related problems [2]}, [3]}, [4]}. Another way to include physics within machine learning approaches has been done through the input features. For instance, [5]} showed in their works that scaling input features based on prior knowledge of the physics involved leads to far better results than just blindly feeding the NN with all available quantities.
Similarly, [6]} wrote in their paper that "the choice of the input features is crucial" to obtain data-based RANS models with acceptable fidelity. These are just two examples of many works demonstrating that successful DL approaches for physical modeling depend on the judicious choice of input quantities. Therefore, the adequate definition of the input features is a central question for data-driven modeling.
